AIMC Topic: Middle Aged

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Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank.

Nature communications
Understanding how risk factors interact to jointly influence disease risk can provide insights into disease development and improve risk prediction. Here we introduce survivalFM, a machine learning extension to the widely used Cox proportional hazard...

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning-Based Prediction Models in a Retrospective Study.

JMIR cancer
BACKGROUND: Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to ...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

Science advances
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcircu...

Evaluating changes in hypoglossal nerve stimulator use over time and long-term adherence.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Evaluate patient phenotypes and longitudinal patterns of hypoglossal nerve stimulator (HGNS) use and identify predictors of long-term HGNS adherence.

Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer.

Scientific reports
This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dy...

Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.

Genome medicine
BACKGROUND: Allogeneic hematopoietic stem cell transplantation (HSCT) is potentially curative for hematologic malignancies but is frequently complicated by relapse and immune-mediated complications, such as graft-versus-host disease (GVHD). Emerging ...

Electrocardiographic sex index: a continuous representation of sex.

Biology of sex differences
Clinical risk calculators consider sex as a binary variable. However, sex is a complex trait with anatomic, physiologic, and metabolic attributes that are not easily summarized in this manner [1]. We propose a continuous representation of sex, the EC...

Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

BMC anesthesiology
BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause significant challenges to perioperative workflow. We describe the development of a machine-learning derived model to predict UIAs using only widely u...

Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.

Lipids in health and disease
BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine l...

Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions.

BMC medical imaging
OBJECTIVES: This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.